DR MIR IQBAL FAHEEM

Academic Administration & Leadership, Faculty/Researcher - Civil Engineering and Consultant – ADR (Arbitrator & Mediator), PQP & QA/QC, Lead Auditor - ISO 9001:2015, promoting technology to the world for the benefit of the society.

EDUCATION

Ph.D (Civil Engineering), MBA (HR)

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering, Building and Construction, Geotechnical Engineering and Engineering Geology, Management Science and Operations Research

Scopus Publications

A methodological framework for road accident severity prediction for indian highways using machine learning models
Humera Khanum, Anshul Garg, Mir Iqbal Faheem, Rushikesh Kulkarni
Methodsx, 2025
This study introduces a methodological framework for predicting road accident severity using a SHAP-enhanced Machine Learning model. Road traffic accidents remain a major global concern, with India reporting over 150,000 fatalities annually. Traditional models fail to capture the complex relationships among various risk factors. This research applies machine learning, specifically Random Forest and Gradient Boosting, to identify and analyse key factors influencing accident severity. SHAP values are used to enhance model interpretability, providing insights into the contribution of each feature.•Develop a Random Forest model and a Gradient Boosting model to predict road accident severity based on a comprehensive set of features.•Utilise SHAP to identify and rank the importance of features, such as vehicle type, weather, and road conditions.•Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Polynomial curve fits are used only as post-hoc visualizations of the Actual-Predicted relationship (on ordinal codes), not as classifier evaluation metrics.The findings highlight that factors like vehicle type, accident location, and road conditions significantly influence accident severity. This approach provides a scalable and interpretable framework for improving road safety on Indian highways, offering data-driven insights for proactive safety measures and infrastructure enhancements.
Accident severity prediction modeling for road safety using random forest algorithm: an analysis of Indian highways
Humera Khanum, Anshul Garg, Mir Iqbal Faheem
F1000research, 2023
Background: Road accidents claim around 1.35 million lives annually, with countries like India facing a significant impact. In 2019, India reported 449,002 road accidents, causing 151,113 deaths and 451,361 injuries. Accident severity modeling helps understand contributing factors and develop preventive strategies. AI models, such as random forest, offer adaptability and higher predictive accuracy compared to traditional statistical models. This study aims to develop a predictive model for traffic accident severity on Indian highways using the random forest algorithm. Methods: A multi-step methodology was employed, involving data collection and preparation, feature selection, training a random forest model, tuning parameters, and evaluating the model using accuracy and F1 score. Data sources included MoRTH and NHAI. Results: The classification model had hyperparameters ‘max depth’: 10, ‘max features’: ‘sqrt’, and ‘n estimators’: 100. The model achieved an overall accuracy of 67% and a weighted average F1-score of 0.64 on the training set, with a macro average F1-score of 0.53. Using grid search, a random forest Classifier was fitted with optimal parameters, resulting in 41.47% accuracy on test data. Conclusions: The random forest classifier model predicted traffic accident severity with 67% accuracy on the training set and 41.47% on the test set, suggesting possible bias or imbalance in the dataset. No clear patterns were found between the day of the week and accident occurrence or severity. Performance can be improved by addressing dataset imbalance and refining model hyperparameters. The model often underestimated accident severity, highlighting the influence of external factors. Adopting a sophisticated data recording system in line with MoRTH and IRC guidelines and integrating machine learning techniques can enhance road safety modeling, decision-making, and accident prevention efforts.
Schedule Delay Modeling of Large Infrastructure Projects in India
Mohammed Abdul Wajid Siddiqui, Mir Iqbal Faheem
Lecture Notes in Civil Engineering, 2022
Bracka verses Byar’s two-stage repair in proximal hypospadias associated with severe chordee: a randomized comparative study
Sajad Ahmad Wani, Aejaz Ahsan Baba, Gowhar Nazir Mufti, Kumar Abdul Rashid, Nisar Ahmad Bhat, Mudasir Buch, Mir Faheem
Pediatric Surgery International, 2020
Least - cost scheduling of Construction Projects
International Journal of Earth Sciences and Engineering, 2010
Construction in challenging environments using intelligent techniques
V. S. S. Kumar, Mir Iqbal Faheem
Earth and Space 2006 Proceedings of the 10th Biennial International Conference on Engineering Construction and Operations in Challenging Environments, 2006
2 Abstract: Present day construction and in its associated activities is an involved process due to the phenomenal increase in various facets of constructional activities. The complexity of the decision parameters adds new dimension in the decision making process for technology transfer. Thus, the need to rationalize the various inputs and optimize the construction planning practices becomes imperative. Several considerations that are qualitatively described by the terms such as high, medium, low, good, bad and satisfactory have an important bearing on the engineering aspects of projects and the structures therein. The subjective judgments implicit in these qualitative terms cannot be directly incorporated into the analysis and planning of engineering activities in a routine way through classical evaluation. The fuzzy set theory makes available a convenient and meaningful tool to the practising engineers to incorporate these seemingly vague but practically powerful factors in the several phases of a project life cycle. This paper reports a methodology for incorporating the fuzzy set concept into the procedural analysis of planning the buildings in challenging environments with or without proper weightages assigned for the qualitative factors. Intangible, non-quantitative and linguistic variables are considered for the evaluation process relatively in an easier way through this concept.